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362 T. Tagami et al.
In one study, hydroxypropyl cellulose/Kollidon VA 64-based floating tablets
were prepared by FDM 3D printer. The Box-Behnken model was used for DoE,
and two DoEs were separately applied (Vo et al.
2020). The authors set several
factors of the printing structure in one DoE (shell thickness, top/bottom thickness,
infill density) to obtain the output (floating force and drug dissolution). Then,
they set the factors of tablet dimensions in another DoE (diameter, height, and
thickness) to obtain the same output. Good correlation between floating force and
drug dissolution of 3D printed tablets and printing parameter was successfully
obtained.
In another study, 3D printed mini-caplets containing baclofen were prepared
by FDM 3D printer, and the formulations were composed of PVA/sorbitol-based
filament (Palekar et al.
2019). The authors mentioned that medication errors with
children are often due to suboptimal efficacy because extemporaneous formulations
for children can be prepared by manipulating adult formulations. Dose adjustment
using 3D printing holds promise for addressing this problem. In this study, 3D
printed mini-caplets were prepared by changing parameters such as infill percentage
(30, 65, 100%) and pattern (hexagonal, sharkfill, linear, diamond), and then drug
dissolution was evaluated. The response surface plot against drug dissolution was
obtained by using DoE (3
2
full functional design). The results showed that the size
and infill density could affect the drug dissolution positively.
Diclofenac-loaded PVA was prepared by hot-melt extrusion, and the pharmaceutical quality of the resulting properties of 3D printed PVA-based tablets for which
immediate drug release was expected was assessed by DoE (Cris
an et al. 2022).
,
The authors conducted a risk analysis to determine the quality of the resulting 3D
printed tablets and drew an Ishikawa diagram to explain the factors. In the study,
three factors (inner structure of tablet, tablet size, and layer height) were set, and
there were four parameters of output, drug contents, disintegration time, and amount
of drug dissolution at 5 and 10 min. D-optimal experimental design was adopted in
the DoE. The design spaces for tablets containing 30, 40, and 50 mg API, for which
more than 55% drug release was expected at 5 min and more than 80% drug release
at 10 min, were obtained.
3D printed polycaprolactone (PCL)-based implant containing dexamethasone
was prepared by FDM 3D printing (Dos Santos et al.
2021). In the study, 2
full factorial design containing three factors (the amount of drug, the amount of
mannitol, and the infill percentage) and two levels was used as the DoE. The
drug release constant and the time required to reach 10% and 20% were set as
the response. The multiple regression analysis equation indicated that the pore
former in the implant (i.e., water-soluble compounds such as mannitol) affected
the drug release, while infill percentage modulated drug release. In another study,
the influence of printing parameters on the resulting 3D printed placebo tablets
was investigated for three polymers (ABS, high-impact polystyrene, PLA) (Pires
et al.
2020). Results were collected for the printlet’s average mass, mass variation
coefficient, printing time, and porosity as a preliminary investigation.
DoE has been utilized for the production of polypills prepared using a PAM
3D printer (Alayoubi et al.
2022). Polypills were composed of a metoprolol-loaded
3
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 363
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core/shell compartment and an atorvastatin-loaded compartment. The authors varied
the amount of Carbopol loading in the core, HPMC loading in the shell, and
number of shell layers. The extrudability of drug formulations was assessed, and
data on several properties (flow pressure, compression rate, non-recoverable strain,
and elastic and plastic flow) were collected. The mechanical properties of the drug
formulation and 3D printed polypills were analyzed. Carbopol concentration in the
core affected the flow pressure and elastic/plastic flow ratio. Additionally, HPMC
affected the flow pressure due to its hydrogel property. The authors also provided
useful information about the extrudability of printer ink from a PAM 3D printer.
Pareto charts were provided on the influence of geometric and formulation variables
on drug dissolution. The number of shells and HPMC amount negatively affected
the drug release, whereas the void in atorvastatin-loaded 3D printed tablet positively
affected.
Zidan et al. focused on the rheological properties of printer ink to predict
its extrudability by PAM 3D printer (Zidan et al.
2019a, b). They prepared
printer ink composed of Carbopol, polyplasdone, microcrystalline cellulose, and
diclofenac. The printing pressure and viscoelastic properties were analyzed by a
texture analyzer and rheometer. The composition and nozzle diameter affected the
parameters of the Herschel-Bulkley equation, yielding stress, instantaneous elastic
displacement, and compression rate. The resulting response surface and contour plot
were obtained by DoE.
A binder jetting 3D printer was used for the preparation of ketoprofen-loaded
tablets (Kreft et al.
2022). In this study, designs containing five process parameters
were selected, and three responses (hardness, friability, and disintegration time)
were chosen. First, two level fractional factorial design was set as the screening
phase, and 11 experiments were conducted. Then D-optimal design was set, and
seven additional experiments were conducted for the optimization phase. The
influence of ink composition was also evaluated.
Madzarevic et al. conducted a study to predict drug release from 3D printed
tablets, and theirs was one of the key studies including both DoE and machine
learning (Madzarevic et al.
2019). 3D printed tablets were fabricated by DLP 3D
printing. Printer inks were composed of ibuprofen, PEGDA, PEG400, water, and
riboflavin as a photo-initiator. 3D printing was conducted by changing the exposure
time to light following the DoE. Physical and mechanical properties of tablets
including weight, diameter, thickness, hardness, and drug loading were determined.
The prediction of drug dissolution was conducted by D-optimal design, but the
authors concluded that the proposed mathematical model was not significant. They
also used two artificial neural networks with different hidden layers. Although
the machine learning model exhibited a high accuracy when predicting the drug
dissolution profile (R
2), the f
factor, which is an index of similarity of drug dissolution between the
2
2
= 0.9811, neural network 1; R2 = 0.9960, neural network
predicted and experimental results, was 52.15 for neural network 1 and 44.91 for
neural network 2. The same group investigated the effect of tablet thickness and
drug loading on the drug release from PEGDA-based tablets prepared by DLP 3D
364 T. Tagami et al.
printer (Stanojevi´cetal.2021). The application of a generalized regression neural
network could predict the release of atomoxetine.
11.6 Future Outlook: Artificial Intelligence (AI) Pharmacists
and 3D Printing Technology
In this section, we comment on the possible future roles of AI in pharmaceutical
work and the relationship with 3D printers as related to AI. The work of pharmacists
is becoming more digitalized: online pharmacies such as Amazon Pharmacy have
emerged, and the efficiency of pharmacist operations is improving. Many of the
tasks carried out by pharmacists in their work are routine tasks, and it is thought
that there are parts that could be easily completed by AI, but ultimately, it seems
ideal for human work and AI work to be carried out separately.
For example, a pharmacist dispenses a certain drug formulation when the
prescription is given by a doctor. There is a distinction between the work performed
by AI and the work performed by pharmacists, and it seems that AI could be in
charge of some simplified tasks, such as dispensing prescribed medications. On
the other hand, advanced skills that only humans can perform, such as observing
a patient’s facial expressions and attitude and receiving a patient’s detailed requests
by actually communicating with the patient and providing counseling, will likely
remain the work of human pharmacists. In addition, by having AI take charge of
some part of the work, it is conceivable that human pharmacists will be able to use
the extra time to develop other aspects of their job.
The effective use of AI has been mentioned in healthcare systems, and emerging
AI technology is expected to be implemented in hospitals and pharmacies for
(1) maintaining patients’ medical records, (2) helping with treatment decisionmaking, (3) assisting with repetitive tasks such as imaging (e.g., X-ray, computed
tomography scan), (4) providing medication-related health support, and (5) acting
as a total healthcare system incorporating personal data and information (Das et al.
2021).
The use of machine learning in healthcare may also require ethical considerations
(Chen et al.
pipeline stages: problem selection, data collection, outcome definition, algorithm
development, and post-development consideration. In the stages, the article mentions that the incautious use of machine learning may lead to injustices pertaining
to race, scientific workforce, gender, and global health. Although machine learning
may be used for medical decision-making and big data analysis for the method of
next generation, the algorithms may reflect unfair human biases (Char et al.
As the trained models in machine learning are considered a “black box,” it is hard to
address issues of transparency (Davenport and Kalakota
mistakes, it is difficult to pin responsibility on the AI.
If pharmaceutical manufacturing using 3D printers, which are related to AI, is
introduced to the medical field, it is conceivable that preparing custom-made drugs
by 3D printer could be one commercial application as well as that by dispensing
2021). The development of machine learning is separated into five
2018).
2019), and if an AI makes
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 365
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Fig. 11.5 Future digital pharmacy where human pharmacist and artificial intelligence cooperate
together
robots (Fig. 11.5). Currently, compounding robots that dispense anticancer drugs
and robots that package pharmaceutical tablets are already on the market. The
automated compounding of chemotherapeutic ingredients can reduce compounding
errors and costs (Batson et al.
into
the manufacturing of custom-made pharmaceuticals. In addition, the concept of
a digital pharmacy using 3D printers has been proposed (Araújo et al.
e
xpected that the production of custom-made drugs using 3D printers, which was
2020). 3D printers are also expected to be incorporated
2019). It is
previously thought to be impossible due to complexity, will become possible with
AI technology.
11.7 Conclusions
Currently, the use of 3D printing technology in various medical fields is highly
anticipated, and research is being actively pursued in this area. 3D printers are
considered to be compatible with other digital technologies, and it is expected
that the development of custom-made drugs will be further promoted by fusing
technologies from different fields such as data science. Collaborating with other
fields outside of medicine can be expected to lead to the development of innovative
pharmaceuticals.
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